Critical care : the official journal of the Critical Care Forum
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There is a high degree of uncertainty regarding optimum care of patients with potential or known intake of oral anticoagulants and traumatic brain injury (TBI). Anticoagulation therapy aggravates the risk of intracerebral hemorrhage but, on the other hand, patients take anticoagulants because of an underlying prothrombotic risk, and this could be increased following trauma. Treatment decisions must be taken with due consideration of both these risks. ⋯ Diagnosis, coagulation testing, and reversal of anticoagulation were considered as key steps upon presentation. Post-trauma management (prophylaxis for thromboembolism and resumption of long-term anticoagulation therapy) was also explored. The lack of robust evidence on which to base treatment recommendations highlights the need for randomized controlled trials in this setting.
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Mechanical ventilation is strongly associated with cognitive decline after critical illness. This finding is particularly evident among older individuals who have pre-existing cognitive impairment, most commonly characterized by varying degrees of cerebral amyloid-β accumulation, neuroinflammation, and blood-brain barrier dysfunction. We sought to test the hypothesis that short-term mechanical ventilation contributes to the neuropathology of cognitive impairment by (i) increasing cerebral amyloid-β accumulation in mice with pre-existing Alzheimer's disease pathology, (ii) increasing neurologic and systemic inflammation in wild-type mice and mice with pre-existing Alzheimer's disease pathology, and (iii) increasing hippocampal blood-brain barrier permeability in wild-type mice and mice with pre-existing Alzheimer's disease pathology. ⋯ These results provide the first evidence that short-term mechanical ventilation independently promotes the neuropathology of Alzheimer's disease in subjects with and without pre-existing cerebral Alzheimer's disease pathology. Future studies are needed to further clarify the specific mechanisms by which this occurs and to develop neuroprotective mechanical ventilation strategies that mitigate the risk of cognitive decline after critical illness.
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Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI). ⋯ Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.
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Rapid response teams (RRTs) respond to hospitalized patients experiencing clinical deterioration and help determine subsequent management and disposition. We sought to evaluate and compare the prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) for prediction of in-hospital mortality following RRT activation. We secondarily evaluated a subgroup of patients with suspected infection. ⋯ The HEWS has comparable clinical accuracy to NEWS2 for prediction of in-hospital mortality among RRT patients.
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Abstract